Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

HRSS SEMESTER 2 > HRSS SEM 2 > Flashcards

HRSS SEM 2 Flashcards

1
Q

Cross-sectional study design

A

Observational or descriptive
Collects data from a population at 1 specific point in time
Groups determined by existing differences, not random allocation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Advantages of cross-sectional study design (5)

A
  • Snapshot of a population at one time
  • Can draw inferences from existing relationships or differences
  • Large numbers of subjects
  • Relatively inexpensive
  • Can generate odds ratio, absolute/relative risk and prevalence
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Disadvantages of cross-sectional study designs

A
  • Results are static: no sequence of events
  • Doesn’t randomly sample
  • Can’t establish cause and effect relationship
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Pearson’s correlation co-efficient

A

Measures linear relationship between 2 variables
P=0 suggests no linear relationship
Coefficients offer crude linear association and are unable to adjust for other variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Regression modelling

A

Investigates if an association exists between variables of interest
Measures strength and direction of association between variables
Studies the form of relationships

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

How are continuous linear relationships examined

A

By linear or non-linear regression models

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

How are categorial outcomes examined

A

By logistic regression

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Describe Linear Regression

A

Ho = no relationship between DV and IV

IV and DV must be continuous; IV can be continuous or categorical

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Assumptions for linear regression

A

Linear relationship between DV and iV
Observation independently and randomly selected
Effects are additive
Homogeneity of variances
Residuals are independent + normally distributed
Absence of outliers and multi-collinearity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Describe skewness and kurtosis, mean/median/mode for a normally distributed variable

A

both = 0
- the further the value is from 0 the more likely it is that the variable isn’t normally distributed
mean median and mode should be equal for a normally distributed variable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Tests of normality

A

Tests of normality (Shapiro-Wilk) compare the shape of the sample distribution to the shape of a normally distributed curve

  • non significant tests suggest distribution of sample isn’t sig different from normal distribution
  • significant tests suggest distribution in question is sig different from a normal distribution
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Multicollinearity

A

Refers to IV’s that are correlated with other IV’s

In presence of multi-collinearity, regression models may not give valid estimates of individual predictors

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Variance inflation factor (VIF)

A

Measure of how much the variance of the estimated regression coefficient is inflated by the existence of correlation among IV’s in the model
VIF = 1 : no correlation among predictors
VIF >4 : warrants further investigation
VIF > 10: signs of serious multicollinearity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

In a simple linear regression model if B > 0 …

A

Positive association between IV and DV

For each unit increase in IV, the DV would increase by (B) value units.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Building a regression model

A

If IV associated with outcome (DV) and no affected by multi-collinearity, then can build multivariable multiple linear regression model for DV
Fitted regression model presents regression coefficients representing adjusted associations between DV and IV; adjusted for each other

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Interpreting a regression model

A

For each unit increase in IV, the estimated DV unit would increase by (B) value, after adjusting for other IV’s
May change association

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

R^2

A

% of variability explained by fitted model

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Observational studies

A

Subjects observed in natural state; can be measure and tested by no intervention or treatment

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Cohort longitudinal studies

A

Population of subjects identified by common link
Researcher can follow across time to see what happens
Useful for establishing natural Hx of a condition
Cohort can be divided at outset into subgroups of people whose experience is to be compared
Can identify those most likely to develop outcome

20
Q

Internal comparison

A

1 cohort involved study

Sub classified and internal comparison done

21
Q

External comparison

A

> 1 cohort in study for purpose of comparison

22
Q

Strengths of cohort studies

A

Can give incidence rate and risk
Cause-Effect
Good when exposure is rare
Minimises selection and information bias

23
Q

Weaknesses

A

Loss to follow up
Requires large sample
Ineffective for rare disease
Time-consuming and expensive

24
Q

Chi Square Test (+ assumption)

A

Determines if there is a significant relationship between 2 categorial variables
Assumption: expected frequency in each cell >5

25
Q

Fisher’s exact test

A

Used if Chi Square Test doesn’t meet assumption (i.e. cells have frequency < 5)

26
Q

Independent samples t-test

A

Used to compare means of a normally distributed continuous variable for 2 groups

27
Q

Odds Ratio

A

Measure of strength of association between exposure and outcome. Represents odds an outcome will occur given a particular exposure; compared to outcome occurring in absence of exposure
OR = 1: exposure doesn’t affect odds of outcome
OR = >1 : exposure associated with higher odds of outcome
OR = <1 : exposure associated with lower odds of outcome

28
Q

Confidence Intervals and OR

A

If CI of OR contains 1 (null value of OR) the relationship is likely to be insignificant
If CI of OR doesn’t contain 1 the relationship is likely to be significant

29
Q

Logistic Regression

A

A regression w an outcome variable that is categorical and IV that can be a mix of continuous/categorical
Predicts which of the possible events are going to happen given certain other information on IV
Identifies factors that determine whether an individual is likely to benefit from a certain type of rehab program/outcome

30
Q

Logistic Regression Assumptions

A

Ration of cases to variables (enough responses in a given category)
Linearity in the logic (regression equation should have a linear relationship w the logic form of the outcome)
Absence of multicollinearity and outliers
Independence of results

31
Q

Logistic Regression Models

A

Dichotomous outcome - Binary LR
Polychromous outcome - multinomial LR
Ordered outcome - ordinal logistic regression

32
Q

What test to examine relationship between outcome variable and DV

A

Chi Square test
Fishers test
T-Test

33
Q

LR - Omnibus Test

A

Asks if null model is an improvement

If p<0.05 then fitted model (new model) as a whole fits significantly better than a null model w/o a predictor

34
Q

LR model summary

A
  • 2 log likelihood suggests new model > null model as in a better fit
  • Nagelkerke R^2 value shows how much variation is explained by new model
35
Q

Goodness of fit test

A

Used to examine if estimated LR model fits sample data
P<0.05 = poor fit
p>0.05 = good fit

36
Q

RCT

A

Individuals allocated at random to receive one of number of interventions (min 2)
= chance of allocation to each intervention
NOT determined by researched, NOT predictable

37
Q

Random allocation

A

Eliminates bias
Allows researchers to make casual inferences - randomisation ensures any difference between groups is due to chance
Covariates are distributed across groups at baseline = unbiased distribution of confounders

38
Q

Sources of bias in RCT

A

Selection bias - inadequate concealment of allocation/incorrect generation of randomisation sequence
Performance bias - inadequate blinding/masking
Detection bias
Attrition bias
Reporting bias

39
Q

How are sample and effect size related

A

Sample size inversely associated with effect size

40
Q

Intention to treat analysis

A

Compares treatment groups as originally allocated, irrespective of whether patients received or adhered to treatment protocol
Promotes external validity

41
Q

Per protocol analysis

A

Compares treatment groups as originally allocated but includes only those patients who completed treatment protocol which compromises internal validity

42
Q

Longitudinal Data Analysis

A

Assesses change in response variable over time, measures temporal patterns of response to Rx, identifies factors that influence change

  1. Mixed effects model - compares individual change over time
  2. Marginal mode (GEE) - compares population over time, evaluates interventions/informs public policy
43
Q

Generalised Estimating Equations (GEE)

A

GEE an extension of general linear model of statistical regression for modelling clustered/correlated data
Offers robust estimate of standard errors to allow for clustering of observations
Produces consistent estimates of regression coefficients and their standard errors
Can deal with normal and non-normal outcome data
If GEE is significant, two interventions are significantly different

44
Q

Assumptions of GEE

A

Assumptions of general linear model
Responses from known family of distribution with specified mean and variance where variance is function of mean
Mean is a linear function of predictors
A correlation structure for the responses must be specified
Any missing data are either missing completely at random or data is missing at random

45
Q

Sources of variation

A

In LDA the unexplained variation is divided into components, making the ultimate error variance smaller
LDA decreases unexplained variability in response - provides better estimates of effect

46
Q

Post estimation distribution of residuals

A

Insignificant p value supports normality of residuals, no significant difference between residuals

HRSS SEMESTER 2 (1 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions